1. Field of the Invention
The present invention relates to medical care delivery, and in particular to improving medical care delivery by improving decision-making skills of medical personnel with systems that simulate interactions with a mentor or a patient using a virtual patient or virtual mentor or both.
2. Description of the Related Art
Medical care delivery is major industry in the United States. An important component of medical care delivery is a cadre of trained medical care professionals who can diagnose patient condition and prescribe treatment protocols using cognitive skills. The fewer errors made by this cadre, the better is the medical care delivered. Currently, cognitive skills of medical care professionals are developed by close interaction with other members of the profession who are already highly skilled and serve as mentors. Of those already skilled only some are good mentors. The more people trained by the good mentors, the better the medical care delivered.
Individuals who are both good physicians and good mentors have limited time, energy and patience to effectively train all medical care profession students and professionals undergoing continuing medical education, collectively called herein medical trainees. To compound the problem, trainees have a limited time to train. Both mentors and trainees would greatly benefit from an automated system that takes on some or most of this burden.
Recently, medical trainees have had their workweek limited to 80 hours. Under this constraint, they must safely complete their required patient care duties while still fulfilling the requirements of a rigorous educational program. In addition, the content of this educational program must evolve continuously by being updated and re-prioritized given the remarkable progress in areas such as the human genome project and molecular biology. Physicians are expected to remain broadly knowledgeable and specifically expert, yet there are no good automated methods to support accomplishing these expectations. Currently, physicians learn information through reading, conferences, one-on-one discussions with experts who are effective mentors, and trial-and-error management of real patients. Safer and more efficient methods for becoming expert and reducing error rates prior to patient contact are desirable.
A number of expert systems have been developed to capture the expertise of medical professionals. Some of these expert systems have been used in the training of students preparing for the medical profession. Training strategies emanate from the work of Ericsson, K. A. (Ed.) (1996). The Road to Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports and Games. Mahwah, N.J., Lawrence: Lawrence Erlbaum Associates. These strategies indicate that the acquisition of expertise in skills and cognitive knowledge is, among other factors, strongly related to deliberate sessions to rehearse well-defined tasks and to obtain immediate feedback designed around ways to improve performance. These strategies have been applied to technical skills training in medicine through the simulation of manual procedures, such as insertion of a chest tube, in part-task trainers. Progress in this area has been slow due to the complex nature of the simulation. Application of these strategies to cognitive skills has been even more difficult, as evidenced by the extensive work done and few resultant successes for expert systems, intelligent tutors and similar systems.
While prior approaches serve as suitable aids for some procedures and isolated organ systems, these expert systems suffer some deficiencies. For example, accepted practice dictates one or a few certain sequences of steps to isolate and treat a problem, and expert systems capture this standard practice. Existing training systems, to varying degrees, capture one or more standard practices and reward a trainee who follows them. Little feedback is provided to a student who deviates from accepted practice, other than to stop the process and report a failure.
However, patient care is a complex problem and procedures change as new understanding and new technology become available. Therefore teaching medical trainees to follow accepted practice by rote is not good training for understanding how the standard practice evolved and is likely to evolve into the future. Furthermore, a patient with multiple conditions may involve different practices that may be confusing to implement together, or, even worse, incompatible for combination. For understanding sufficient to function at such levels, it is preferable to teach trainees to address patient care as discovering the most effective treatment for a complex problem using a variety of knowledge sources, information gathering procedures, incremental logic, treatments and interventions—a cognitive process used by the most successful practitioners and successfully taught by the more successful mentors. In some cases, a successful mentor stops the trainee who is deviating too far, in some cases a successful mentor suggests alternatives to consider, and in some rare cases that do not endanger a patient, the mentor may let the trainee arrive at the accepted response through a circuitous route of self discovery.
Based on the foregoing, and other aspects of the problem described in more detail below, there is a clear need for automated systems that take on some or most of the burden of conveying cognitive skills to medical trainees.